Cancer is one of the major causatives of death in the world. The overall prevalence rate of cancer is about 1% of the population and yearly incidence rate is about 0.5%. About one out of ten patients discharged from hospitals have cancer as their primary diagnosis. The main existing treatment modalities are surgical resection, radiotherapy, chemotherapy, and biological therapy including hormonal therapy. Furthermore, newly developed biotechnologies have been offering new treatment modalities, such as gene therapy. Nevertheless, cancer is dreaded disease because in most cases there is no really effective treatment available. One of the major difficulties of cancer treatment is the ability of cancer cells to become resistant to drugs and to spread to other sites of tissues, where they can generate new tumors, which often results in recurrence. If a cancer recurrence is predictable before recurrence occurs, such cancer becomes curable by local treatment with surgery.
Among various tumors, hepatocellular carcinoma (hereinafter referred to as HCC) is one of the most common fatal cancers in the world and the number of incidences is increasing in many countries including the USA, Japan, China and European countries. Both hepatitis B virus (hereinafter referred to as HBV) and hepatitis C virus (hereinafter referred to as HCV) infections can be a causative of HCC. In fact, increase in HCC patients is in parallel to an increase in chronic HCV infection (El-Serag, H. B. & Mason, A. C. Rising incidence of hepatocellular carcinoma in the United States, N. Engl. J. Med. 340, 745-750 (1999) and Okuda, K. Hepatocellular carcinoma, J. Hepatol. 32, 225-237 (2000)). Despite the elevated incidences of HCC, there is no promising therapy for this disease. The major problem in the treatment of HCC is intrahepatic metastasis. Recurrence was observed in 30 to 50% of HCC patients who had received hepatic resection (Iizuka, N. et al. NM23-H1 and NM23-H2 messenger RNA abundance in human hepatocellular carcinoma, Cancer Res. 55, 652-657 (1995), Yamamoto, J. et al. Recurrence of hepatocellular carcinoma after surgery, Br. J. Surg. 83, 1219-1222 (1996), and Poon, R. T. et al. Different risk factors and prognosis for early and late intrahepatic recurrence after resection of hepatocellular carcinoma, Cancer 89, 500-507 (2000)). Although the pathologic TNM staging system has been applied in the treatment of HCC, this system is poorly predictive of recurrences in patients who undergo hepatic resection (Izumi, R. et al. Prognostic factors of hepatocellular carcinoma in patient undergoing hepatic resection, Gastroenterology 106, 720-727 (1994)). A number of molecules have also been proposed as predictive markers for HCCs, none of them has proven to be clinically useful (Iizuka, N. et al. NM23-H1 and NM23-H2 messenger RNA abundance in human hepatocellular carcinoma, Cancer Res. 55, 652-657 (1995), Hsu, H. C. et al. Expression of p53 gene in 184 unifocal hepatocellular carcinomas: association with tumor growth and invasiveness, Cancer Res. 53, 4691-4694 (1993), and Mathew, J. et al. CD44 is expressed in hepatocellular carcinomas showing vascular invasion, J. Pathol. 179, 74-79 (1996)). Thus, any method to predict recurrence would be quite valuable to understand cancer mechanisms and also to establish the new therapies for cancer. However, because there are technological limitations for predicting recurrence by the traditional methods and further limitations may be attributable to high inter-patient heterogeneity of tumors, it is necessary to devise a novel method to characterize tumors and predict cancer recurrence.
Recent development of microarray technologies, which allow one to perform parallel expression analysis of a large number of genes, has opened up a new era in medical science (Schena, M. et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science 270, 467-470 (1995), and DeRisi J. et al. Use of a cDNA microarray to analyze gene expression patterns in human cancer, Nature Genet. 14, 457-460 (1996)). In particular, studies by cDNA microarrays of the gene expression of tumors have provided significant insights into the properties of malignant tumors such as prognosis and drug-sensitivity (Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature 403, 503-511 (2000), and Scherf, U. et al. A gene expression database for the molecular pharmacology of cancer, Nature Genet. 24, 236-244 (2000)).
Recently, supervised learning has been introduced into gene-expression analysis (Brazma, A & Vilo, J. Gene expression data analysis, FEBS Lett. 480, 17-24 (2000) and Kell, D. B. & King, R. D. On the optimization of classes for the assignment of unidentified reading frames in functional genomics programs: the need for machine learning, Trends Biotechnol. 18, 93-98 (2000)). Using classified samples, supervised learning has the conclusive advantage of much a priori knowledge about the nature of the data (Duda, R. O. et al. Pattern classification, John Wiley & Sons (2001), and Jain, A. K. et al. Statistical pattern recognition: A review, IEEE Trans. Pattern Analysis and Machine Intelligence. 22, 4-37 (2000)). However, none of supervised learning methods previously published directly evaluates the combination of genes and thus can utilize information concerning the statistical characteristics, i.e., structure of the distribution of genes (Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science 286, 531-537 (1999), and Brown, M. P. et al. Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Natl. Acad. Sci. USA 97, 262-267 (2000)).
Scoring systems that are predictive of cancer recurrence are created by analyzing the DNA microarray data with supervised learning in statistical pattern recognition (Duda, R. O. et al. Pattern classification, John Wiley & Sons (2001)).
Supervised learning in statistical pattern recognition has been successfully applied to resolve a variety of issues such as document classification, speech recognition, biometric recognition, and remote sensing (Jain, A. K. et al. Statistical pattern recognition: A review, IEEE Trans, Pattern Analysis and Machine Intelligence. 22, 4-37 (2000)).
In the present invention, the inventors provide a scoring system to predict cancer recurrence by analyzing the expression of genes and/or proteins of human primary tumors. That is the invention concerns a method for the prediction of cancer recurrence which comprises measuring the expression of genes and/or proteins of human tumor tissues, and comparing it with the expression of the genes and/or proteins of human primary tumors from patients who have cancer recurrence and those who do not have cancer recurrence.